{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wHsFZo6akD8M",
        "outputId": "7208f68d-b547-420b-8cc7-09aa16e2a6c7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
        "batch_size = 32\n",
        "train_dataset = torchvision.datasets.cifar.CIFAR100(root='cifar100', train=True, transform=transform, download=True)\n",
        "test_dataset = torchvision.datasets.cifar.CIFAR100(root='cifar100', train=True, transform=transform, download=True)\n",
        "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
        "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# functions to show an image\n",
        "\n",
        "\n",
        "def imshow(img):\n",
        "    img = img / 2 + 0.5     # unnormalize\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "# get some random training images\n",
        "dataiter = iter(train_loader)\n",
        "# images, labels = dataiter.next()\n",
        "images, labels = next(dataiter)\n",
        "\n",
        "# show images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "# print labels\n",
        "print(labels.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "VjkCK8NykPMb",
        "outputId": "d25bda93-96c7-4f51-86b6-22b96089d169"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([32])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.nn import init\n",
        "\n",
        "def conv3x3(in_planes, out_planes, stride=1):\n",
        "    \"3x3 convolution with padding\"\n",
        "    return nn.Conv2d(\n",
        "        in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False\n",
        "    )\n",
        "\n",
        "\n",
        "class BasicBlock(nn.Module):\n",
        "    expansion = 1\n",
        "\n",
        "    def __init__(\n",
        "        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n",
        "    ):\n",
        "        super(BasicBlock, self).__init__()\n",
        "        self.conv1 = conv3x3(inplanes, planes, stride)\n",
        "        self.bn1 = nn.BatchNorm2d(planes)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "        self.conv2 = conv3x3(planes, planes)\n",
        "        self.bn2 = nn.BatchNorm2d(planes)\n",
        "        self.downsample = downsample\n",
        "        self.stride = stride\n",
        "\n",
        "        if use_triplet_attention:\n",
        "            self.triplet_attention = TripletAttention(planes, 16)\n",
        "        else:\n",
        "            self.triplet_attention = None\n",
        "\n",
        "    def forward(self, x):\n",
        "        residual = x\n",
        "\n",
        "        out = self.conv1(x)\n",
        "        out = self.bn1(out)\n",
        "        out = self.relu(out)\n",
        "\n",
        "        out = self.conv2(out)\n",
        "        out = self.bn2(out)\n",
        "\n",
        "        if self.downsample is not None:\n",
        "            residual = self.downsample(x)\n",
        "\n",
        "        if not self.triplet_attention is None:\n",
        "            out = self.triplet_attention(out)\n",
        "\n",
        "        out += residual\n",
        "        out = self.relu(out)\n",
        "\n",
        "        return out\n",
        "      \n",
        "class Bottleneck(nn.Module):\n",
        "    expansion = 4\n",
        "\n",
        "    def __init__(\n",
        "        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n",
        "    ):\n",
        "        super(Bottleneck, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(planes)\n",
        "        self.conv2 = nn.Conv2d(\n",
        "            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False\n",
        "        )\n",
        "        self.bn2 = nn.BatchNorm2d(planes)\n",
        "        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n",
        "        self.bn3 = nn.BatchNorm2d(planes * 4)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "        self.downsample = downsample\n",
        "        self.stride = stride\n",
        "\n",
        "        if use_triplet_attention:\n",
        "            self.triplet_attention = TripletAttention(planes * 4, 16)\n",
        "        else:\n",
        "            self.triplet_attention = None\n",
        "\n",
        "    def forward(self, x):\n",
        "        residual = x\n",
        "\n",
        "        out = self.conv1(x)\n",
        "        out = self.bn1(out)\n",
        "        out = self.relu(out)\n",
        "\n",
        "        out = self.conv2(out)\n",
        "        out = self.bn2(out)\n",
        "        out = self.relu(out)\n",
        "\n",
        "        out = self.conv3(out)\n",
        "        out = self.bn3(out)\n",
        "\n",
        "        if self.downsample is not None:\n",
        "            residual = self.downsample(x)\n",
        "\n",
        "        if not self.triplet_attention is None:\n",
        "            out = self.triplet_attention(out)\n",
        "\n",
        "        out += residual\n",
        "        out = self.relu(out)\n",
        "\n",
        "        return out\n",
        "\n",
        "class Res2NetBottleneck(nn.Module):\n",
        "\n",
        "    expansion = 4\n",
        "\n",
        "    def __init__(\n",
        "        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False, scales=4\n",
        "    ):\n",
        "        super(Res2NetBottleneck, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(planes)\n",
        "        self.conv2 = nn.ModuleList([nn.Conv2d(planes/scales, planes/scales, kernel_size=3, stride=stride, padding=1, bias=False) for _ in range(scales-1)])\n",
        "        self.bn2 = nn.ModuleList([nn.BatchNorm2d(planes // scales) for _ in range(scales-1)])\n",
        "        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n",
        "        self.bn3 = nn.BatchNorm2d(planes * 4)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "        self.downsample = downsample\n",
        "        self.stride = stride\n",
        "        self.scales = scales\n",
        "\n",
        "        if use_triplet_attention:\n",
        "            self.triplet_attention = TripletAttention(planes * 4, 16)\n",
        "        else:\n",
        "            self.triplet_attention = None\n",
        "\n",
        "    def forward(self, x):\n",
        "        residual = x\n",
        "\n",
        "        out = self.conv1(x)\n",
        "        out = self.bn1(out)\n",
        "        out = self.relu(out)\n",
        "\n",
        "        xs = torch.chunk(out, self.scales, 1)\n",
        "        ys = []\n",
        "        for s in range(self.scales):\n",
        "            if s == 0:\n",
        "                ys.append(xs[s])\n",
        "            elif s == 1:\n",
        "                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s]))))\n",
        "            else:\n",
        "                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s] + ys[-1]))))\n",
        "        out = torch.cat(ys, 1)\n",
        "\n",
        "        out = self.conv3(out)\n",
        "        out = self.bn3(out)\n",
        "\n",
        "        if self.downsample is not None:\n",
        "            residual = self.downsample(x)\n",
        "\n",
        "        if not self.triplet_attention is None:\n",
        "            out = self.triplet_attention(out)\n",
        "\n",
        "        out += residual\n",
        "        out = self.relu(out)\n",
        "\n",
        "        return out    \n",
        "\n",
        "class ResNet(nn.Module):\n",
        "    def __init__(self, block, layers, network_type, num_classes, att_type=None):\n",
        "        self.inplanes = 64\n",
        "        super(ResNet, self).__init__()\n",
        "        self.network_type = network_type\n",
        "        # different model config between ImageNet and CIFAR\n",
        "        if network_type == \"ImageNet\":\n",
        "            self.conv1 = nn.Conv2d(\n",
        "                3, 64, kernel_size=7, stride=2, padding=3, bias=False\n",
        "            )\n",
        "            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
        "            self.avgpool = nn.AvgPool2d(7)\n",
        "        else:\n",
        "            self.conv1 = nn.Conv2d(\n",
        "                3, 64, kernel_size=3, stride=1, padding=1, bias=False\n",
        "            )\n",
        "\n",
        "        self.bn1 = nn.BatchNorm2d(64)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "\n",
        "        self.layer1 = self._make_layer(block, 64, layers[0], att_type=att_type)\n",
        "        self.layer2 = self._make_layer(\n",
        "            block, 128, layers[1], stride=2, att_type=att_type\n",
        "        )\n",
        "        self.layer3 = self._make_layer(\n",
        "            block, 256, layers[2], stride=2, att_type=att_type\n",
        "        )\n",
        "        self.layer4 = self._make_layer(\n",
        "            block, 512, layers[3], stride=2, att_type=att_type\n",
        "        )\n",
        "\n",
        "        self.fc = nn.Linear(512 * block.expansion, num_classes)\n",
        "\n",
        "        init.kaiming_normal_(self.fc.weight)\n",
        "        for key in self.state_dict():\n",
        "            if key.split(\".\")[-1] == \"weight\":\n",
        "                if \"conv\" in key:\n",
        "                    init.kaiming_normal_(self.state_dict()[key], mode=\"fan_out\")\n",
        "                if \"bn\" in key:\n",
        "                    if \"SpatialGate\" in key:\n",
        "                        self.state_dict()[key][...] = 0\n",
        "                    else:\n",
        "                        self.state_dict()[key][...] = 1\n",
        "            elif key.split(\".\")[-1] == \"bias\":\n",
        "                self.state_dict()[key][...] = 0\n",
        "\n",
        "    def _make_layer(self, block, planes, blocks, stride=1, att_type=None):\n",
        "        downsample = None\n",
        "        if stride != 1 or self.inplanes != planes * block.expansion:\n",
        "            downsample = nn.Sequential(\n",
        "                nn.Conv2d(\n",
        "                    self.inplanes,\n",
        "                    planes * block.expansion,\n",
        "                    kernel_size=1,\n",
        "                    stride=stride,\n",
        "                    bias=False,\n",
        "                ),\n",
        "                nn.BatchNorm2d(planes * block.expansion),\n",
        "            )\n",
        "\n",
        "        layers = []\n",
        "        layers.append(\n",
        "            block(\n",
        "                self.inplanes,\n",
        "                planes,\n",
        "                stride,\n",
        "                downsample,\n",
        "                use_triplet_attention=att_type == \"TripletAttention\",\n",
        "            )\n",
        "        )\n",
        "        self.inplanes = planes * block.expansion\n",
        "        for i in range(1, blocks):\n",
        "            layers.append(\n",
        "                block(\n",
        "                    self.inplanes,\n",
        "                    planes,\n",
        "                    use_triplet_attention=att_type == \"TripletAttention\",\n",
        "                )\n",
        "            )\n",
        "\n",
        "        return nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv1(x)\n",
        "        x = self.bn1(x)\n",
        "        x = self.relu(x)\n",
        "        if self.network_type == \"ImageNet\":\n",
        "            x = self.maxpool(x)\n",
        "\n",
        "        x = self.layer1(x)\n",
        "        x = self.layer2(x)\n",
        "        x = self.layer3(x)\n",
        "        x = self.layer4(x)\n",
        "\n",
        "        if self.network_type == \"ImageNet\":\n",
        "            x = self.avgpool(x)\n",
        "        else:\n",
        "            x = F.avg_pool2d(x, 4)\n",
        "        x = x.view(x.size(0), -1)\n",
        "        x = self.fc(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "def ResidualNet(network_type, depth, num_classes, att_type):\n",
        "\n",
        "    assert network_type in [\n",
        "        \"ImageNet\",\n",
        "        \"CIFAR10\",\n",
        "        \"CIFAR100\",\n",
        "    ], \"network type should be ImageNet or CIFAR10 / CIFAR100\"\n",
        "    assert depth in [18, 34, 50, 101], \"network depth should be 18, 34, 50 or 101\"\n",
        "\n",
        "    if depth == 18:\n",
        "        # model = ResNet(BasicBlock, [2, 2, 2, 2], network_type, num_classes, att_type)\n",
        "        model = ResNet(Bottleneck, [2, 2, 2, 2], network_type, num_classes, att_type)\n",
        "\n",
        "    elif depth == 34:\n",
        "        # model = ResNet(BasicBlock, [3, 4, 6, 3], network_type, num_classes, att_type)\n",
        "        model = ResNet(Bottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n",
        "\n",
        "    elif depth == 50:\n",
        "        model = ResNet(Bottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n",
        "\n",
        "    elif depth == 101:\n",
        "        model = ResNet(Bottleneck, [3, 4, 23, 3], network_type, num_classes, att_type)\n",
        "\n",
        "    return model\n",
        "  \n",
        "def Res2Net(network_type, depth, num_classes, att_type):\n",
        "\n",
        "    assert network_type in [\n",
        "        \"ImageNet\",\n",
        "        \"CIFAR10\",\n",
        "        \"CIFAR100\",\n",
        "    ], \"network type should be ImageNet or CIFAR10 / CIFAR100\"\n",
        "    assert depth in [18, 34, 50, 101], \"network depth should be 18, 34, 50 or 101\"\n",
        "\n",
        "    if depth == 18:\n",
        "        model = ResNet(Res2NetBottleneck, [2, 2, 2, 2], network_type, num_classes, att_type)\n",
        "\n",
        "    elif depth == 34:\n",
        "        model = ResNet(Res2NetBottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n",
        "\n",
        "    elif depth == 50:\n",
        "        model = ResNet(Res2NetBottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n",
        "\n",
        "    elif depth == 101:\n",
        "        model = ResNet(Res2NetBottleneck, [3, 4, 23, 3], network_type, num_classes, att_type)\n",
        "\n",
        "    return model\n"
      ],
      "metadata": {
        "id": "1mY2iwmCkcpA"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device=\"cuda\"\n",
        "net=ResidualNet(\"CIFAR100\",18,100,None).to(device)\n",
        "print(net)\n",
        "res2net=Res2Net(\"CIFAR100\",18,100,None).to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "otA9CIgdnXfW",
        "outputId": "3c9fe9be-f2a4-4390-d91a-9c6e17bdfd25"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ResNet(\n",
            "  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (relu): ReLU(inplace=True)\n",
            "  (layer1): Sequential(\n",
            "    (0): Bottleneck(\n",
            "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (downsample): Sequential(\n",
            "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (1): Bottleneck(\n",
            "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (layer2): Sequential(\n",
            "    (0): Bottleneck(\n",
            "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (downsample): Sequential(\n",
            "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (1): Bottleneck(\n",
            "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (layer3): Sequential(\n",
            "    (0): Bottleneck(\n",
            "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (downsample): Sequential(\n",
            "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (1): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (layer4): Sequential(\n",
            "    (0): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (downsample): Sequential(\n",
            "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (1): Bottleneck(\n",
            "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (fc): Linear(in_features=2048, out_features=100, bias=True)\n",
            ")\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-31-928b6388ff3e>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mResidualNet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"CIFAR100\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mres2net\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mRes2Net\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"CIFAR100\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-30-839b66e73219>\u001b[0m in \u001b[0;36mRes2Net\u001b[0;34m(network_type, depth, num_classes, att_type)\u001b[0m\n\u001b[1;32m    297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdepth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m18\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 299\u001b[0;31m         \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mResNet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRes2NetBottleneck\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnetwork_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_classes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0matt_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mdepth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m34\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-30-839b66e73219>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, block, layers, network_type, num_classes, att_type)\u001b[0m\n\u001b[1;32m    179\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReLU\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0matt_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0matt_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    182\u001b[0m         self.layer2 = self._make_layer(\n\u001b[1;32m    183\u001b[0m             \u001b[0mblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstride\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0matt_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0matt_type\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-30-839b66e73219>\u001b[0m in \u001b[0;36m_make_layer\u001b[0;34m(self, block, planes, blocks, stride, att_type)\u001b[0m\n\u001b[1;32m    221\u001b[0m         \u001b[0mlayers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m         layers.append(\n\u001b[0;32m--> 223\u001b[0;31m             block(\n\u001b[0m\u001b[1;32m    224\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minplanes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    225\u001b[0m                 \u001b[0mplanes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-30-839b66e73219>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, inplanes, planes, stride, downsample, use_triplet_attention, scales)\u001b[0m\n\u001b[1;32m    113\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConv2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minplanes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplanes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBatchNorm2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplanes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModuleList\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConv2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplanes\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mscales\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplanes\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mscales\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstride\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscales\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModuleList\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBatchNorm2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplanes\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mscales\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscales\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConv2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplanes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplanes\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-30-839b66e73219>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    113\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConv2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minplanes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplanes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBatchNorm2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplanes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModuleList\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConv2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplanes\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mscales\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplanes\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mscales\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstride\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscales\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModuleList\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBatchNorm2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplanes\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mscales\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscales\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConv2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplanes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplanes\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, padding_mode, device, dtype)\u001b[0m\n\u001b[1;32m    448\u001b[0m         \u001b[0mpadding_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpadding\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpadding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0m_pair\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpadding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    449\u001b[0m         \u001b[0mdilation_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_pair\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdilation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 450\u001b[0;31m         super().__init__(\n\u001b[0m\u001b[1;32m    451\u001b[0m             \u001b[0min_channels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_channels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_size_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstride_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdilation_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    452\u001b[0m             False, _pair(0), groups, bias, padding_mode, **factory_kwargs)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, in_channels, out_channels, kernel_size, stride, padding, dilation, transposed, output_padding, groups, bias, padding_mode, device, dtype)\u001b[0m\n\u001b[1;32m    135\u001b[0m                 (in_channels, out_channels // groups, *kernel_size), **factory_kwargs))\n\u001b[1;32m    136\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m             self.weight = Parameter(torch.empty(\n\u001b[0m\u001b[1;32m    138\u001b[0m                 (out_channels, in_channels // groups, *kernel_size), **factory_kwargs))\n\u001b[1;32m    139\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: empty() received an invalid combination of arguments - got (tuple, dtype=NoneType, device=NoneType), but expected one of:\n * (tuple of ints size, *, tuple of names names, torch.memory_format memory_format, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)\n * (tuple of ints size, *, torch.memory_format memory_format, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.optim as optim\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(net.parameters(), lr=0.005)\n",
        "print(len(train_loader))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "35VI01P_kqal",
        "outputId": "4e3f699e-5064-4b1c-86bc-39ddcacadaeb"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1563\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ck0do7nrmNTQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "def train(net, epoch):\n",
        "    net.train()\n",
        "    # Loop over each batch from the training set\n",
        "    train_tqdm = tqdm(train_loader, desc=\"Epoch \" + str(epoch))\n",
        "    for batch_idx, (data, target) in enumerate(train_tqdm):\n",
        "        # Copy data to GPU if needed\n",
        "        data = data.to(device)\n",
        "        target = target.to(device)\n",
        "        # Zero gradient buffers\n",
        "        optimizer.zero_grad()\n",
        "        # Pass data through the network\n",
        "        output = net(data)\n",
        "        # Calculate loss\n",
        "        loss = criterion(output, target)\n",
        "        # Backpropagate\n",
        "        loss.backward()\n",
        "        # Update weights\n",
        "        optimizer.step()  # w - alpha * dL / dw\n",
        "        train_tqdm.set_postfix({\"loss\": \"%.3g\" % loss.item()})\n",
        "    return net\n",
        "\n",
        "def validate(net, lossv,top1AccuracyList,top5AccuracyList):\n",
        "    net.eval()\n",
        "    val_loss = 0\n",
        "    top1Correct = 0\n",
        "    top5Correct = 0\n",
        "    for index,(data, target) in enumerate(test_loader):\n",
        "        data = data.to(device)\n",
        "        labels = target.to(device)\n",
        "        outputs = net(data)\n",
        "        val_loss += criterion(outputs, labels).data.item()\n",
        "        _, top1Predicted = torch.max(outputs.data, 1)\n",
        "        top5Predicted = torch.topk(outputs.data, k=5, dim=1, largest=True)[1]\n",
        "        top1Correct += (top1Predicted == labels).cpu().sum().item()\n",
        "        label_resize = labels.view(-1, 1).expand_as(top5Predicted)\n",
        "        top5Correct += torch.eq(top5Predicted, label_resize).view(-1).cpu().sum().float().item()\n",
        "\n",
        "    val_loss /= len(test_loader)\n",
        "    lossv.append(val_loss)\n",
        "\n",
        "    top1Acc=100*top1Correct / len(test_loader.dataset)\n",
        "    top5Acc=100*top5Correct / len(test_loader.dataset)\n",
        "    top1AccuracyList.append(top1Acc)\n",
        "    top5AccuracyList.append(top5Acc)\n",
        "    \n",
        "    print('\\nValidation set: Average loss: {:.4f}, Top1Accuracy: {}/{} ({:.0f}%) Top5Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
        "        val_loss, top1Correct, len(test_loader.dataset), top1Acc,top5Correct, len(test_loader.dataset), top5Acc))\n",
        "#     accuracy = 100. * correct.to(torch.float32) / len(testloader.dataset)\n",
        "#     accv.append(accuracy)"
      ],
      "metadata": {
        "id": "xKlx2mCxlE02"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "lossv = []\n",
        "top1AccuracyList = []   # top1准确率列表\n",
        "top5AccuracyList = []   # top5准确率列表\n",
        "max_epoch = 5\n",
        "training_time = 0.0\n",
        "for epoch in range(max_epoch):  # loop over the dataset multiple times\n",
        "    start = time.time()\n",
        "    net = train(net, epoch)\n",
        "    end = time.time()\n",
        "    training_time += end-start\n",
        "    with torch.no_grad():\n",
        "        validate(net, lossv,top1AccuracyList,top5AccuracyList)\n",
        "\n",
        "print('Finished Training')\n",
        "print('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pTWz8B3Dl9gu",
        "outputId": "565792d7-7ca9-4980-bb4d-ee5c0081577c"
      },
      "execution_count": 38,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch 0: 100%|██████████| 1563/1563 [01:57<00:00, 13.34it/s, loss=2.35]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 1.6340, Top1Accuracy: 27612/50000 (55%) Top5Accuracy: 41959.0/50000 (84%)\n",
            "\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch 1: 100%|██████████| 1563/1563 [01:58<00:00, 13.16it/s, loss=1.78]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 1.2981, Top1Accuracy: 31782/50000 (64%) Top5Accuracy: 44711.0/50000 (89%)\n",
            "\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch 2: 100%|██████████| 1563/1563 [01:58<00:00, 13.18it/s, loss=0.616]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 1.0250, Top1Accuracy: 35300/50000 (71%) Top5Accuracy: 46480.0/50000 (93%)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 3: 100%|██████████| 1563/1563 [01:58<00:00, 13.14it/s, loss=1.57]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Validation set: Average loss: 0.7417, Top1Accuracy: 39519/50000 (79%) Top5Accuracy: 48211.0/50000 (96%)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 4: 100%|██████████| 1563/1563 [01:59<00:00, 13.11it/s, loss=0.637]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Validation set: Average loss: 0.5436, Top1Accuracy: 42157/50000 (84%) Top5Accuracy: 49029.0/50000 (98%)\n",
            "\n",
            "Finished Training\n",
            "Training complete in 9m 53s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "res2netLossv = []\n",
        "res2netTop1AccuracyList = []   # top1准确率列表\n",
        "res2netTop5AccuracyList = []   # top5准确率列表\n",
        "max_epoch = 5\n",
        "training_time = 0.0\n",
        "for epoch in range(max_epoch):  # loop over the dataset multiple times\n",
        "    start = time.time()\n",
        "    res2net = train(res2net, epoch)\n",
        "    end = time.time()\n",
        "    training_time += end-start\n",
        "    with torch.no_grad():\n",
        "        validate(res2net, lossv,top1AccuracyList,top5AccuracyList)\n",
        "\n",
        "\n",
        "        \n",
        "print('Finished Training')\n",
        "print('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))"
      ],
      "metadata": {
        "id": "5qEPmaZO1NU3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 702
        },
        "outputId": "9547e481-57ce-4ed7-e577-c7d4384cba17"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 0: 100%|██████████| 1563/1563 [01:53<00:00, 13.79it/s, loss=5.41]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Validation set: Average loss: 5.4151, Top1Accuracy: 524/50000 (1%) Top5Accuracy: 2511.0/50000 (5%)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 1: 100%|██████████| 1563/1563 [01:53<00:00, 13.76it/s, loss=5.72]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Validation set: Average loss: 5.3904, Top1Accuracy: 521/50000 (1%) Top5Accuracy: 2505.0/50000 (5%)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 2: 100%|██████████| 1563/1563 [01:53<00:00, 13.72it/s, loss=5.76]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Validation set: Average loss: 5.3740, Top1Accuracy: 522/50000 (1%) Top5Accuracy: 2480.0/50000 (5%)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 3: 100%|██████████| 1563/1563 [01:53<00:00, 13.77it/s, loss=5.78]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Validation set: Average loss: 5.3192, Top1Accuracy: 524/50000 (1%) Top5Accuracy: 2490.0/50000 (5%)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 4:  59%|█████▊    | 917/1563 [01:06<00:46, 13.77it/s, loss=5.4]\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-39-1c9ae22a30cd>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_epoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# loop over the dataset multiple times\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m     \u001b[0mres2net\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres2net\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mtraining_time\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-36-6de92ee7862a>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(net, epoch)\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0;31m# Pass data through the network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m         \u001b[0;31m# Calculate loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-10-72a0625031fe>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    251\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer4\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnetwork_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"ImageNet\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    215\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    218\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-10-72a0625031fe>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/batchnorm.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0;31m# TODO: if statement only here to tell the jit to skip emitting this when it is None\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_batches_tracked\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# type: ignore[has-type]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_batches_tracked\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[has-type]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmomentum\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# use cumulative moving average\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m                     \u001b[0mexponential_average_factor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.0\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_batches_tracked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 绘制曲线\n",
        "plt.figure(figsize=(5,3))\n",
        "plt.plot(np.arange(1,len(lossv)), lossv)\n",
        "plt.title('validation loss')\n",
        "\n",
        "plt.figure(figsize=(5,3))\n",
        "plt.plot(np.arange(1,len(top1AccuracyList)), top1AccuracyList)\n",
        "plt.title('validation top1 accuracy')\n",
        "\n",
        "plt.figure(figsize=(5,3))\n",
        "plt.plot(np.arange(1,len(top5AccuracyList)), top5AccuracyList)\n",
        "plt.title('validation top5 accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z1-D3LkhvzDq",
        "outputId": "e29901ea-e0b2-4141-f18e-a6431171ec0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Validation set: Average loss: 4.5184, Top1Accuracy: 943/10000 (9%) Top5Accuracy: 0.0/10000 (0%)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fyWGvlbHup5w"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}